Machine learning applied to remote sensing in the context of intertidal zone mapping: A literature review
Bibliographic record
Abstract
Accurate digital elevation models for intertidal zones are essential for coastal management, yet traditional survey methods and models based on normalized index from remote sensing products often struggle to represent these dynamic environments. Recently, machine learning has emerged as a promising alternative for mapping intertidal morphology. This study systematically reviewed 60 articles using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and bibliometric analysis, focusing on machine learning applications for intertidal mapping. Among 19 identified machine learning models: U-Net (convolutional architecture) and Random Forest (decision tree architecture) delivered the most accurate results. Model performance was highly dependent on environmental characteristics; in general, wider tidal ranges reduced accuracy, while substrates with clearer spectral signatures improved it. Machine learning approaches consistently outperformed models without machine learning in representing complex coastal morphology. Most studies used open-source Landsat or Sentinel imagery; however, commercial satellites and high-resolution video cameras, though less common, significantly improved model performance. Validation strategies based on high-resolution data from active sensors such as LiDAR (Light Detection and Ranging) proved essential to assess the accuracy and reliability of predictions, especially in heterogeneous environments. This comprehensive review highlights not only the most effective model architecture but also key research gaps, such as the limited exploration of temporal dynamics, the underrepresentation of macrotidal settings, and the challenges of transferring models across regions. Future research should prioritize convolution-based architectures trained with multi-source, high-resolution datasets, integrate UAV and active sensor data for robust validation, and explore scalable solutions to enhance the generalizability of machine learning models for intertidal zone mapping. • First systematic bibliometric review on intertidal topographic inversion using remote sensing and ML models to generate intertidal DEMs. • Sixty peer-reviewed studies analyzed using PRISMA, with bibliometric mapping and model performance comparison (e.g., RMSE, accuracy). • Random Forest and U-Net models outperformed others especially when applied to high-resolution imagery and validated with high resolution in-situ data. • Environmental and methodological factors - including tidal regime, spatial scale, sensor resolution and bottom type affect model performance. • Provides practical guidance on model selection , dataset design, and validation, while identifying gaps in transferability and uncertainty handling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".